import cv2
import torch
import onnx
import onnxruntime
import numpy as np

import time

# codeformer converted to onnx
# using https://github.com/redthing1/CodeFormer


class CodeFormerEnhancer:
    def __init__(self, model_path="codeformer.onnx", device="cpu"):
        model = onnx.load(model_path)
        session_options = onnxruntime.SessionOptions()
        session_options.graph_optimization_level = (
            onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
        )
        providers = ["CPUExecutionProvider"]
        if device == "cuda":
            providers = [
                ("CUDAExecutionProvider", {"cudnn_conv_algo_search": "DEFAULT"}),
                "CPUExecutionProvider",
            ]
        self.session = onnxruntime.InferenceSession(
            model_path, sess_options=session_options, providers=providers
        )

    def enhance(self, img, w=0.9):
        img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
        img = img.astype(np.float32)[:, :, ::-1] / 255.0
        img = img.transpose((2, 0, 1))
        nrm_mean = np.array([0.5, 0.5, 0.5]).reshape((-1, 1, 1))
        nrm_std = np.array([0.5, 0.5, 0.5]).reshape((-1, 1, 1))
        img = (img - nrm_mean) / nrm_std

        img = np.expand_dims(img, axis=0)

        out = self.session.run(
            None, {"x": img.astype(np.float32), "w": np.array([w], dtype=np.double)}
        )[0]
        out = (out[0].transpose(1, 2, 0).clip(-1, 1) + 1) * 0.5
        out = (out * 255)[:, :, ::-1]

        return out.astype("uint8")